This is a project page for our works in Deep Learning for Retinal Images Analysis. Spolight Video

  • WACV 2021, ACCV Workshop 2018, and ICML Workshop 2018

“DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation”

PDF, Github

WACV 2021

“A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases”

PDF, Github

  • ICML 2018, Workshop of Computational Biology

  • Authors: C.-H. Huck Yang, Jia-Hong Huang, Fangyu Liu, Fang-Yi Chiu, Mengya Gao, Weifeng Lyu, I-Hung Lin M.D., Jesper Tegner

“Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model”

PDF, Github

  • ACCV 2018, Workshop of AI for Retina Images Analysis

  • Authors: C.-H. Huck Yang, Fangyu Liu, Jia-Hong Huang, Meng Tian, Hiromasa Morikawa, I-Hung Lin, Yi-Chieh Liu, Hao-Hsiang Yang, Jesper Tegner

The figure shows the result of U-Net effects on clinical images with different morphological shapes.

“Synthesizing New Retinal Symptom Images by Multiple Generative Models”, oral, AIRIA, ACCV’18

PDF, Github

  • ACCV 2018, Workshop of AI for Retina Images Analysis, Oral

  • Authors: Yi-Chieh Liu, Hao-Hsiang Yang, Chao-Han Huck Yang, Jia-Hong Huang, Meng Tian, Hiromasa Morikawa, Yi-Chang James Tsai, Jesper Tegner